CN102198003A - Limb movement detection and evaluation network system and method - Google Patents
Limb movement detection and evaluation network system and method Download PDFInfo
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Abstract
The invention discloses a limb movement detection and evaluation network system comprising a sensing network system consisting of a central processing unit and at least two sensing units connected to the central processing unit. The invention also discloses a limb movement detection and evaluation method. The invention has the beneficial effects as follows: with application of the limb movement detection and evaluation network system and method, a user can arrange training with reference to a target movement curve in a system template library, and can also detect and evaluate limb movement per se; besides, the cost is low, and the time is also saved.
Description
Technical field
The present invention relates to a kind of detection evaluating system and method thereof, relate in particular to a kind of limb motion and detect assessment network system and method thereof.
Background technology
The user does some limb motions, need practise, and need to detect and assess the achievement of exercise often.If engage the coach or the counselor of specialty, needed expense is huge.And the user only is the exercises of the various video datas of contrast, and to whether moving standard, motion problem such as whether get a desired effect also needs just can finish by the professional.
For example apoplexy (being commonly called as apoplexy) can cause user's dyskinesia, destroys user's sports coordination, and the research that a large amount of cranial nerve are learned proves that the apoplexy user of survival can recover their certain motor capacity and technical ability by rehabilitation training.Traditional apoplexy rehabilitation scheme, often by the clinical rehabilitation doctor, the nurse provides at convalescent clinic or care centre, owing to relate to man-to-man professional rehabilitation training, labour intensive costs an arm and a leg.Investigation statistics shows that in the totle drilling cost of apoplexy user's treatment management, the expense of hospital and sanatorium has accounted for maximum ratio.And studies show that, early go out hospital and carry out reconditioning at home, can produce similar rehabilitation result, and under many circumstances, than the routine clinical nursing fortunately, also greatly reduce the cost of nursing supervision simultaneously.Therefore, at home the remote rehabilitation system has become the emphasis of the research and development of academia and industry in recent years.
At present, though there are many limb motion aid systems (as the house rehabilitation system) to be suggested, but much be to have adopted comparatively complicated and expensive recovery robot system, and these existing systems lack motility and adaptability, can not follow the tracks of patient's state and rehabilitation progress, only have limited people/machine communication function.Simultaneously, acceleration/inertial sensor also is applied in family endowment and the rehabilitation system gradually, and the application of this type of pick off at present mainly concentrates on: the old man drops to warning, and is used for obtaining the attitude of user at training process.But these pick offs are to use separately, do not form a network system and come coordinate operation.
Therefore, also need both need the plenty of time for the detection of limb motion (dancing, wushu, limb rehabilitating or the like) assessment at present in a large number by manpower, mint of money again, cost is higher.
Summary of the invention
Technical problem to be solved by this invention is to provide a kind of limb motion to detect assessment network system and method thereof, utilize the present invention for the user, train according to the target trajectory curve in the system template storehouse, perhaps self limb motion is detected and assesses.
For realizing purpose of the present invention, the invention provides a kind of limb motion and detect the assessment network system, comprising: CPU, and the sensing network system that is connected at least two sensing units compositions of CPU.
Sensing unit attaches on the limbs, is used for detecting and record limbs real time kinematics parameter, and is transferred to CPU;
CPU is used to receive the limbs real time kinematics parameter of sensing unit transmission, and handles and assess the kinestate of limbs according to the limbs real time kinematics parameter that receives.
More preferably, limb motion of the present invention detects the assessment network system, and sensing unit comprises: inertial sensor and sensing communication module.
Inertial sensor is used for the detection instruction according to CPU, detects and record limbs real time kinematics parameter.
The sensing communication module is used to receive and transmit the detection instruction of CPU, and inertial sensor is sent to CPU according to the limbs real time kinematics parameter that detects command detection and record.
Detect the opening entry instruction that instruction comprises limb motion, and limb motion number of times or time parameter instruction.
More preferably, limb motion of the present invention detects the assessment network system, and inertial sensor is three inertial sensors of XYZ.
More preferably, limb motion of the present invention detects the assessment network system, and CPU comprises: data communication module and data processing module.
Data communication module is used to receive the limbs real time kinematics parameter that the sensing communication module sends; The detection that sends CPU is instructed to sensing unit; The data processed result that also is used to send data processing module is to remote terminal, and the receiving remote instruction.
Data processing module, be used to send the limb motion control instruction, and according to the limb motion parameter that receives, judge the type of sports that limbs carry out, analyze the limb motion cycle, obtain the real time kinematics geometric locus of limb motion, and calculate the quantity of same limb motion type, and assess the assessment result that obtains the limb motion state according to the real time execution geometric locus of limb motion and the quantity of same limb motion type.
More preferably, limb motion of the present invention detects the assessment network system, and CPU also comprises data memory module and display module.
Data memory module, be used to store limb motion type default and that on display module, show and described limb motion type space geometric locus data, and detected limb motion parameter, and the limbs real time kinematics geometric locus that goes out according to the limb motion calculation of parameter and the quantity of same limb motion type, assessment result.
Display module, be used for the control instruction sent according to data processing module, read limb motion type of storing in the data memory module and the target trajectory curve that shows the limb motion type, and according to data processing module limbs real time kinematics parameter is received and evaluation process after fructufy the time show limbs real time kinematics parameter, the times of exercise of same limb motion type, limbs real time kinematics geometric locus, and assessment result.
More preferably, limb motion of the present invention detects the assessment network system, kinematic parameter comprises: the X and the Y-axis angular speed of the limbs of each sensing unit record, the speed of X and Y direction and linear acceleration etc. or X, Y and Z shaft angle speed, the speed of X, Y and Z-direction and linear acceleration.
The present invention also provides a kind of limb motion to detect appraisal procedure, may further comprise the steps:
Steps A attaches to two sensing units on the limbs at least, detects and record limb motion parameter, and is transferred to CPU;
Step B, CPU receives described sensing unit and transmits the limb motion parameter of returning, and handles and assess the kinestate of limbs according to the limb motion parameter that receives.
More preferably, limb motion of the present invention detects appraisal procedure, and steps A may further comprise the steps:
Steps A 2, the number of times or the time of setting limb motion;
Steps A 4, at the limb motion number of times of finishing setting or after the time, the sensing communication module in the sensing unit sends to CPU with the user's that notes limbs real time kinematics parameter.
More preferably, limb motion of the present invention detects appraisal procedure, and is further comprising the steps of between steps A 2 and the A3:
Steps A 21, the data processing module of CPU sends the demonstration control instruction to the display module of CPU, and display module reads the default limb motion type and the described limb motion type space geometric locus data of storing in the data memory module and shows;
Steps A 22, CPU sends detection record and instructs to sensing unit, and limbs move according to the space tracking curve of the limb motion type that steps A 21 shows.
More preferably, limb motion of the present invention detects appraisal procedure, and steps A 21 may further comprise the steps:
Steps A 211 is selected at least a limb motion type from the multiple limb motion type that the display module of CPU shows;
Steps A 212: on the display module of CPU, the selected target trajectory curve of limb motion type space geometric locus data show, the target trajectory curve is to have the level and smooth sinusoidal wave signal of periodicity.
More preferably, limb motion of the present invention detects appraisal procedure, and step B may further comprise the steps:
Step B1, the data communication module of CPU receives the real time kinematics parameter of limb motion, and sends the real time kinematics parameter to the data processing module of CPU and the data memory module of CPU;
Step B2, data memory module stores the real time kinematics parameter; Data processing module carries out the kinestate that limbs were handled and assessed to the limb motion parameter.
More preferably, limb motion of the present invention detects appraisal procedure, among the step B2, carries out the kinestate that limbs were handled and assessed to the limb motion parameter, may further comprise the steps:
Step B21 judges the type of sports that limbs carry out;
Step B22 analyzes the limb motion cycle, obtains the real time kinematics geometric locus of limb motion;
Step B23 calculates the quantity of same limb motion type;
Step B24 assesses the assessment result that obtains the limb motion state according to the real time kinematics geometric locus of limb motion and the quantity of same limb motion type.
More preferably, limb motion of the present invention detects appraisal procedure, and among the step B21, assessment obtains the assessment result of limb motion state, comprises the steps:
Step B211, according at least 6 that receive real-time limb motion parameters, respectively and the kinematic parameter of each type of sports template samples in the default limb motion type sample template base carry out computing cross-correlation, obtain a plurality of cross correlation results;
Step B212 carries out K-arest neighbors classified counting to each cross correlation results, draws the distance of real-time limb motion parameter to each template samples;
Step B213 reads in the described distance labelling of K minimum template samples, and obtains the labelling of real-time limb motion test sample book according to the labelling of this K template samples, thereby judges the affiliated limb motion type of current limb motion.
More preferably, limb motion of the present invention detects appraisal procedure, and step B23 comprises the steps:
Step B231, with detect and the amplitude normalization to 0 of at least 6 real-time limb motion parameters of record to 1;
Step B232 by Fourier transformation or wavelet analysis periodicity analysis method, calculates the periodicity of the actual motion of real-time limb motion;
Step B233, according to the amplitude of user's actual motion, the cycle is drawn the real time kinematics geometric locus;
Step B234, the geometric locus of contrast actual motion and the deviation of target trajectory curve, and the quantity of limb motion are finished the assessment of the kinestate of limb motion, obtain assessment result.
More preferably, limb motion of the present invention detects appraisal procedure, and K value minimum is 11.
More preferably, limb motion of the present invention detects appraisal procedure, and the geometric locus of contrast actual motion and the computational methods that deviation adopted of target trajectory curve are correlation coefficient process among the step B234;
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and correlation coefficient r is:
The value of correlation coefficient r between-1 and+1 between, promptly-1≤r≤+ 1;
| r|=1, expression T ordered series of numbers and S ordered series of numbers are complete linear correlation, are functional relationship, show that actual motion geometric locus and the target trajectory curve of this moment matches;
R=0, expression T ordered series of numbers and S ordered series of numbers do not have linear dependency relation, show that actual motion geometric locus and the target trajectory curve of this moment do not match fully;
| r|＞0, expression T ordered series of numbers is relevant with the S ordered series of numbers; | r| rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
More preferably, limb motion of the present invention detects appraisal procedure, and the geometric locus of contrast actual motion and the computational methods that deviation adopted of target trajectory curve are the mean error quadratic method among the step B234;
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and coefficient of correlation r is:
The numerical value of r is between 0 and 1;
R=0 represents that no actual motion takes place;
R=1 represents that two movement locus match, and the training moving-mass is very high;
R rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
Beneficial effect of the present invention: utilize limb motion of the present invention to detect assessment network system and method, target trajectory curve in the comparable lighting system template base of user is trained, also can detect and assess self limb motion, cost be low, also saves time.
Description of drawings
Fig. 1 is the structural representation of the first embodiment of the present invention;
Actual motion geometric locus and target trajectory curve chart that Fig. 2 draws for the first embodiment of the present invention;
Fig. 3 is the target trajectory curve chart of the second embodiment of the present invention.
The specific embodiment
Clearer in order to make limb motion of the present invention detect purpose, technical scheme and the advantage of assessing network system and method thereof, below in conjunction with concrete drawings and the specific embodiments, limb motion detection assessment network system of the present invention and method thereof are further elaborated.
Fig. 1 is the structural representation of the embodiment of the invention, and as shown in Figure 1, limb motion detects the assessment network system, comprising: CPU 20, and at least two sensing units 10 that are connected to CPU 20.
More preferably, inertial sensor 101 is three inertial sensors of XYZ.
In the present embodiment, inertial sensor 101 is three inertial sensors of XYZ.
In the present embodiment, as a kind of embodiment, described sensing communication module 102 can be a 802.15.4 radio receiving transmitting module, is realized by the XBee module, also can be realized by CC2530 chip or other the suitable ZigBee module/chip of Texas Instrument.
Detect the opening entry instruction that instruction comprises limb motion, and limb motion number of times or time parameter instruction.
In the present embodiment, as a kind of embodiment, described data communication module 201 can be a 802.15.4 radio receiving transmitting module, is realized by the XBee module, also can be realized by CC2530 chip or other the suitable ZigBee module/chip of Texas Instrument.Data processing module 202, data memory module 203 and display module 204 are realized that by a computer data processing module 202 is a computer CPU, and data memory module 203 is a computer hard disc, and display module 204 is a computer monitor.CPU also can be realized by a smart mobile phone or a panel computer.
The limb motion of present embodiment detects appraisal procedure, may further comprise the steps:
Steps A attaches to two sensing units 10 on the limbs at least, detects and record limb motion parameter, and is transferred to CPU 20;
Select three sensing units 10 in the present embodiment for use, the attached and user's wrist of difference is on forearm and the postbrachium.
Step B, CPU 20 receives the limb motion parameter that described sensing unit 10 transmission are returned, and handles and assess the kinestate of limbs according to the limb motion parameter that receives.
The limb motion parameter of passing back in the present embodiment comprises: the attached sensing unit place limbs that three sensing units write down respectively are in the angular speed of X, Y and Z axle, the speed of X, Y and Z-direction and linear acceleration.
More preferably, steps A may further comprise the steps:
Steps A 1 attaches to two sensing units 10 on the limbs that will move at least;
Select three sensing units 10 in the present embodiment for use, attach to user's wrist respectively, on forearm and the postbrachium.
Steps A 2, the number of times or the time of setting limb motion;
Setting the user in the present embodiment finished 5 upper limb sides and lifts in 10 seconds.
Steps A 3, the inertial sensor 101 in the sensing unit 10 detects and writes down the real time kinematics parameter of limbs;
Attach to user's wrist in the present embodiment respectively, the inertial sensor 101 in the sensing unit 10 on forearm and the postbrachium detects respectively and the recording user wrist, and forearm and postbrachium are in the angular speed of X, Y and Z axle, the speed of X, Y and Z-direction and linear acceleration.
Steps A 4, at the limb motion number of times of finishing setting or after the time, the sensing communication module 102 in the sensing unit 10 sends to CPU 20 with the user's that notes limbs real time kinematics parameter.
After the user finished 5 upper limb sides and lifts in the present embodiment in 10 seconds, attach to user's wrist, the sensing communication module 102 in 3 sensing units 10 on forearm and the postbrachium sends to CPU 20 with the user's that notes limbs real time kinematics parameter.
More preferably, further comprising the steps of between steps A 2 and the A3:
Steps A 21, the data processing module 202 of CPU 20 sends the demonstration control instruction to the display module 204 of CPU 20, and display module 204 reads the default limb motion type and the limb motion type space geometric locus data of storage in the data memory module 203 and shows;
Steps A 22, CPU 20 sends detection record and instructs to sensing unit 10, and limbs move according to the space tracking curve of the limb motion type that steps A 21 shows.
More preferably, steps A 21 may further comprise the steps:
Steps A 211 is selected at least a limb motion type from the multiple limb motion type that the display module 204 of CPU 20 shows;
In the present embodiment, the user selects the upper limb side to lift from the multiple limb motion type that display module 204 shows.
Steps A 212: on the display module 204 of CPU 20, the selected target trajectory curve of limb motion type space geometric locus data show, level and smooth sine wave or other smoothed curve that similarly the have periodicity signal of described target trajectory curve to have periodicity.
In the present embodiment, display module 204 shows the target trajectory curve that the upper limb side is lifted.
More preferably, step B may further comprise the steps:
Step B1, the data communication module 201 of CPU 20 receives the real time kinematics parameter of limb motion, and sends the real time kinematics parameter to the data processing module 202 of CPU 20 and the data memory module 203 of CPU 20;
Step B2, data memory module 203 stores the real time kinematics parameter; Data processing module 202 carries out the kinestate that limbs were handled and assessed to the limb motion parameter.
More preferably, among the step B2, carry out the kinestate that limbs were handled and assessed to the limb motion parameter, may further comprise the steps:
Step B21 judges the type of sports that limbs carry out;
In the present embodiment, data processing module 202 is judged the ongoing motion of user by K-arest neighbors classified counting and is lifted for the upper limb side.
Step B22 analyzes the limb motion cycle, obtains the real time kinematics geometric locus of limb motion;
In the present embodiment, data processing module 202 calculates the periodicity of the actual motion of real-time limb motion by the Fourier transformation analytical method.
Step B23 calculates the quantity of same limb motion type;
Step B24 assesses the assessment result that obtains the limb motion state according to the real time kinematics geometric locus of limb motion and the quantity of same limb motion type.
In the present embodiment, data processing module 202 carries out the upper limb side according to the user and lifts the real time kinematics geometric locus of motion and finish the assessment result that quantity assessment that the upper limb side lifts obtains the limb motion state.
More preferably, among the step B21, assessment obtains the assessment result of limb motion state, comprises the steps:
Step B211, according at least 6 that receive real-time limb motion parameters, respectively and the kinematic parameter of each type of sports template samples in the default limb motion type sample template base carry out computing cross-correlation, obtain a plurality of cross correlation results;
In the present embodiment, CPU 20 receives 9 real-time upper extremity exercise parameters, respectively and the kinematic parameter of lifting template samples of the upper limb side in the default limb motion type sample template base carry out computing cross-correlation, obtain a plurality of cross correlation results.
Step B212 carries out K-arest neighbors classified counting to each cross correlation results, draws the distance of real-time limb motion parameter to each template samples;
Step B213 reads in the described distance labelling of K minimum template samples, and obtains the labelling of real-time limb motion test sample book according to the labelling of this K template samples, thereby judges the affiliated limb motion type of current limb motion.
As shown in table 1, the utilization different K values, the different classify accuracy that K-arest neighbors sorting algorithm calculates, K value are 3,5,7,9 o'clock, all can occur once judging by accident, when the K value is 11, erroneous judgement, rate of accuracy reached to 100% no longer occur.In the present embodiment, the K value is 11.
The different classify accuracy tables that table 1K-arest neighbors sorting algorithm calculates
| The |
3 | 5 | 7 | 9 | 11 |
| The |
1 | 1 | 1 | 1 | 0 |
| Accuracy rate (%) | 97.2 | 97.2 | 97.2 | 97.2 | 100 |
More preferably, step B23 comprises the steps:
Step B231, with detect and the amplitude normalization to 0 of at least 6 real-time limb motion parameters of record to 1;
In the present embodiment, with detect and the amplitude normalization to 0 of 9 real-time limb motion parameters of record to 1.
Step B232 by Fourier transformation or wavelet analysis periodicity analysis method, calculates the periodicity of the actual motion of real-time limb motion;
In the present embodiment, adopt the analytical method of Fourier transformation.
Step B233, according to the amplitude of user's actual motion, the cycle is drawn the real time kinematics geometric locus;
Step B234, the geometric locus of contrast actual motion and the deviation of target trajectory curve, and the quantity of limb motion are finished the assessment of the kinestate of limb motion, obtain assessment result.(as shown in Figure 2) actual motion geometric locus and target trajectory curve show simultaneously, can intuitively contrast the actual motion performance.
As a kind of embodiment, in the present embodiment, adopt the geometric locus of correlation coefficient process contrast actual motion and the deviation of target trajectory curve.
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and correlation coefficient r is:
The value of correlation coefficient r between-1 and+1 between, promptly-1≤r≤+ 1;
| r|=1, expression T ordered series of numbers and S ordered series of numbers are complete linear correlation, are functional relationship, show that actual motion geometric locus and the target trajectory curve of this moment matches;
R=0, expression T ordered series of numbers and S ordered series of numbers do not have linear dependency relation, show that actual motion geometric locus and the target trajectory curve of this moment do not match fully;
| r|＞0, expression T ordered series of numbers is relevant with the S ordered series of numbers; | r| rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
As a kind of embodiment, the geometric locus of contrast actual motion and the deviation of target trajectory curve also can adopt the mean error quadratic method.
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and coefficient of correlation r is:
The numerical value of r is between 0 and 1;
R=0 represents that no actual motion takes place;
R=1 represents that two movement locus match, and the training moving-mass is very high;
R rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
More preferably, in order to guarantee the accuracy to the judgement of limb motion type, K value minimum is 11.
In the present embodiment, CPU 20 is carried out the real time kinematics parameter that the upper limb side is lifted motion with the user, the geometric locus of actual motion and assessment result (also can be passed through GPRS/GSM by the Internet, WIFI, means of communication such as 3G) be sent on hospital or rehabilitation center or rehabilitation expert's the server or terminal unit, doctor or rehabilitation expert assess and provide the rehabilitation suggestion according to above-mentioned information to user's current motion, to assess then and suggestion is sent to CPU 20, and be stored in the data memory module 203 and and present to the user by display module 204.
Doctor or rehabilitation expert can also inquiring user carries out the historical record of rehabilitation exercise from data memory module 203 by the Internet (also can pass through GPRS/GSM, WIFI, means of communication such as 3G).
Fig. 3 is the target trajectory curve chart of the second embodiment of the present invention, as shown in Figure 3, has listed four kinds of different classes of upper extremity exercise classifications measuring with three inertial sensors of two XYZ among the figure.Each sports category is at X, and Y has two sets of curves that provided by three inertial sensors of different XYZ (black with gray) with the Z direction.These curves have constituted the feature of discerning these sports category.
The limb motion of the embodiment of the invention detects assessment network system and method, and different target trajectories has different paths, represents different training method and path.The user is following target trajectory and is carrying out training, and as far as possible and target trajectory coincide the minimizing error.The size of error has shown the extent of damage of user's upper extremity function.So promptly can carry out rehabilitation training, also can check the rehabilitation situation according to the assessment result of training.Limb motion of the present invention detects 9 kinematic parameters of assessment network system by inertia sensing unit detection record, gather the retrieving algorithm that carries out match search or carry out other with the template that writes down in advance simultaneously based on signal processing and artificial intelligence, conclude the classification of the ongoing motion of user, and and then calculate and other range deviation of this target class, also real-time simultaneously definite user's upper limb is for the transient position of immobilized relatively health, and the position of these transient states can constitute the actual motion track of upper extremity exercise again; System calculates the periodicity of user's upper extremity exercise by the periodicity analysis to the track of the upper limb serial movement noted, again according to the periodicity of upper extremity exercise, determine that also recording user upper limb arm carries out the number of times of rehabilitation exercise, thereby judge the intensity that the user carries out rehabilitation training.The user then can carry out the repeating motion of certain intensity, i.e. the periodic movement of some according to the actual physical ability of oneself.Also can patient when hospital carries out clinical diagnosis, set in advance, and can in training course of treatment subsequently, adjust at any time by the physiatrician.
The above only is a preferred example of the present invention, is not limited to the present invention, and for a person skilled in the art, the present invention can have various changes and variation.Within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improve, all should be included in protection scope of the present invention.
Claims (17)
1. a limb motion detects the assessment network system, it is characterized in that, comprising: CPU (20), and the sensing network system that is connected at least two sensing units (10) composition of CPU (20);
Described sensing unit (10) attaches on the limbs, is used for detecting and record limbs real time kinematics parameter, and is transferred to CPU (20);
Described CPU (20) is used to receive the limbs real time kinematics parameter of described sensing unit (10) transmission, and handles and assess the kinestate of limbs according to the described limbs real time kinematics parameter that receives.
2. limb motion according to claim 1 detects the assessment network system, it is characterized in that described sensing unit (10) comprising: inertial sensor (101) and sensing communication module (102);
Described inertial sensor (101) is used for the detection instruction according to described CPU (20), detects and record limbs real time kinematics parameter;
Described sensing communication module (102), be used for receiving and transmitting the detection instruction of described CPU (20), and described inertial sensor (101) is sent to described CPU (20) according to the limbs real time kinematics parameter that described detection command detection also writes down;
Described detection instruction comprises the opening entry instruction of limb motion, and limb motion number of times or time parameter instruction.
3. limb motion according to claim 2 detects the assessment network system, it is characterized in that described inertial sensor (101) is three inertial sensors of XYZ.
4. limb motion according to claim 1 detects the assessment network system, it is characterized in that described CPU (20) comprising: data communication module (201) and data processing module (202);
Described data communication module (201) is used to receive the limbs real time kinematics parameter that described sensing communication module (102) sends; The detection that sends described CPU (20) is instructed to described sensing unit (10); The data processed result that also is used to send described data processing module (202) is to remote terminal, and the receiving remote instruction;
Described data processing module (202), be used to send the limb motion control instruction, and according to the limb motion parameter that receives, judge the type of sports that limbs carry out, analyze the limb motion cycle, obtain the real time kinematics geometric locus of limb motion, and calculate the quantity of same limb motion type, and assess the assessment result that obtains the limb motion state according to the real time execution geometric locus of limb motion and the quantity of same limb motion type.
5. limb motion according to claim 4 detects the assessment network system, and described CPU (20) also comprises data memory module (203) and display module (204);
Described data memory module (203), be used to store limb motion type and described limb motion type space geometric locus data default and that on described display module (204), show, and detected limb motion parameter, and the limbs real time kinematics geometric locus that goes out according to the limb motion calculation of parameter and the quantity of same limb motion type, assessment result;
Described display module (204), be used for the control instruction sent according to described data processing module (202), read the limb motion type of storage in the described data memory module (203) and show the target trajectory curve of described limb motion type, and according to described data processing module (202) limbs real time kinematics parameter is received and evaluation process after fructufy the time show limbs real time kinematics parameter, the times of exercise of same limb motion type, limbs real time kinematics geometric locus, and assessment result.
6. limb motion according to claim 1 detects the assessment network system, it is characterized in that, described kinematic parameter comprises: the X and the Y-axis angular speed of the limbs of each sensing unit record, the speed of X and Y direction and linear acceleration etc. or X, Y and Z shaft angle speed, the speed of X, Y and Z-direction and linear acceleration.
7. a limb motion detects appraisal procedure, it is characterized in that, may further comprise the steps:
Steps A attaches to two sensing units (10) on the limbs at least, detects and record limb motion parameter, and is transferred to described CPU (20);
Step B, described CPU (20) receives the limb motion parameter that described sensing unit (10) transmission is returned, and handles and assess the kinestate of limbs according to the described limb motion parameter that receives.
8. limb motion according to claim 7 detects appraisal procedure, it is characterized in that described steps A may further comprise the steps:
Steps A 1 attaches to two sensing units (10) on the limbs that will move at least;
Steps A 2, the number of times or the time of setting limb motion;
Steps A 3, the inertial sensor (101) in the described sensing unit (10) detects and writes down the real time kinematics parameter of limbs;
Steps A 4, at the limb motion number of times of finishing setting or after the time, the sensing communication module (102) in the described sensing unit (10) sends to described CPU (20) with the user's that notes limbs real time kinematics parameter.
9. limb motion according to claim 8 detects appraisal procedure, it is characterized in that, and is further comprising the steps of between described steps A 2 and the A3:
Steps A 21, the data processing module (202) of described CPU (20) sends the demonstration control instruction to the display module (204) of CPU (20), and described display module (204) reads the default limb motion type and the described limb motion type space geometric locus data of storage in the data memory module (203) and shows;
Steps A 22, described CPU (20) send the detection record instruction to described sensing unit (10), and limbs move according to the space tracking curve of the limb motion type that steps A 21 shows.
10. limb motion according to claim 8 detects appraisal procedure, it is characterized in that described steps A 21 may further comprise the steps:
Steps A 211 is selected at least a limb motion type from the multiple limb motion type that the display module (204) of described CPU (20) shows;
Steps A 212: on the display module (204) of described CPU (20), the selected target trajectory curve of described limb motion type space geometric locus data show, level and smooth sine wave or other smoothed curve that similarly the have periodicity signal of described target trajectory curve to have periodicity.
11. limb motion according to claim 7 detects appraisal procedure, it is characterized in that described step B may further comprise the steps:
Step B1, the data communication module (201) of described CPU (20) receives the real time kinematics parameter of limb motion, and sends described real time kinematics parameter to the data processing module (202) of described CPU (20) and the data memory module (203) of described CPU (20);
Step B2, described data memory module (203) stores the real time kinematics parameter; Described data processing module (202) carries out the kinestate that limbs were handled and assessed to the limb motion parameter.
12. limb motion according to claim 11 detects appraisal procedure, it is characterized in that, among the described step B2, carries out the kinestate that limbs were handled and assessed to the limb motion parameter, may further comprise the steps:
Step B21 judges the type of sports that limbs carry out;
Step B22 analyzes the limb motion cycle, obtains the real time kinematics geometric locus of limb motion;
Step B23 calculates the quantity of same limb motion type;
Step B24 assesses the assessment result that obtains the limb motion state according to the real time kinematics geometric locus of limb motion and the quantity of same limb motion type.
13. limb motion according to claim 12 detects appraisal procedure, it is characterized in that, among the described step B21, assessment obtains the assessment result of limb motion state, comprises the steps:
Step B211, according at least 6 that receive real-time limb motion parameters, respectively and the kinematic parameter of each type of sports template samples in the default limb motion type sample template base carry out computing cross-correlation, obtain a plurality of cross correlation results;
Step B212 carries out K-arest neighbors classified counting to each cross correlation results, draws the distance of real-time limb motion parameter to each template samples;
Step B213 reads in the described distance labelling of K minimum template samples, and obtains the labelling of real-time limb motion test sample book according to the labelling of this K template samples, thereby judges the affiliated limb motion type of current limb motion.
14. limb motion according to claim 12 detects appraisal procedure, it is characterized in that described step B23 comprises the steps:
Step B231, with detect and the amplitude normalization to 0 of at least 6 real-time limb motion parameters of record to 1;
Step B232 by Fourier transformation or wavelet analysis periodicity analysis method, calculates the periodicity of the actual motion of real-time limb motion;
Step B233, according to the amplitude of user's actual motion, the cycle is drawn the real time kinematics geometric locus;
Step B234, the geometric locus of contrast actual motion and the deviation of target trajectory curve, and the quantity of limb motion are finished the assessment of the kinestate of limb motion, obtain assessment result.
15. limb motion according to claim 13 detects appraisal procedure, it is characterized in that described K value minimum is 11.
16. limb motion according to claim 14 detects appraisal procedure, it is characterized in that, the geometric locus of contrast actual motion and the computational methods that deviation adopted of target trajectory curve are correlation coefficient process among the described step B234;
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and correlation coefficient r is:
The value of correlation coefficient r between-1 and+1 between, promptly-1≤r≤+ 1;
| r|=1, expression T ordered series of numbers and S ordered series of numbers are complete linear correlation, are functional relationship, show that actual motion geometric locus and the target trajectory curve of this moment matches;
R=0, expression T ordered series of numbers and S ordered series of numbers do not have linear dependency relation, show that actual motion geometric locus and the target trajectory curve of this moment do not match fully;
| r|＞0, expression T ordered series of numbers is relevant with the S ordered series of numbers; | r| rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
17. limb motion according to claim 14 detects appraisal procedure, it is characterized in that, the geometric locus of contrast actual motion and the computational methods that deviation adopted of target trajectory curve are the mean error quadratic method among the described step B234;
The coordinate ordered series of numbers T={{x of target trajectory curve
1, y
1, z
1, { x
2, y
2, z
2..., { x
n, y
n, z
n, the coordinate ordered series of numbers S={{x ' of actual motion geometric locus
1, y '
1, z '
1, x '
2, y '
2, z '
2..., x '
n, y '
n, z '
n, n measures number of samples, and coefficient of correlation r is:
The numerical value of r is between 0 and 1;
R=0 represents that no actual motion takes place;
R=1 represents that two movement locus match, and the training moving-mass is very high;
R rises at 1 o'clock from 0, shows that the quality of hands-on campaign is improving gradually.
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